Name
Abstract | ||
|---|---|---|
From real input/output data, different models of an unmanned aerial vehicle are obtained by applying adaptive neural networks. These models are control-oriented; their main objective is to help us to design, implement and simulate different intelligent controllers and to test them on real systems. The influence of the selected training data on the final model is also discussed. They have been compared to off-line learning neural models with satisfactory results in terms of accuracy and computational cost. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-19719-7_29 | 10TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS |
Keywords | Field | DocType |
Adaptive neural networks,Soft computing,Modeling,Identification,Unmanned aerial vehicles (UAV) | Training set,Neural control,Computer science,Artificial intelligence,Soft computing,Artificial neural network,Real systems,Machine learning | Conference |
Volume | ISSN | Citations |
368 | 2194-5357 | 1 |
PageRank | References | Authors |
0.36 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Enrique Sierra | 1 | 1 | 0.36 |
| Matilde Santos | 2 | 143 | 24.39 |